Comments (6)
Passing in a file as a string doesn't address my concern. Each file has a specific format: each line has a (string, string, int) or something. We have a system to pass in arguments to the app. Use it.
I don't know if this will effect the speed issues. It will certainly help diminish complication.
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Different algorithms take as input different types of files. The format within the file are different. Hence I cannot specify it in yaml in a single format.
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Looking in local/data
I see two file types: one JSON object and many different CSVs with (almost) the same header. I'm assuming the CSV files are the different files? This can be worked around.
In the YAML file you can specify a list of dicts. In each dict you can specify the keys. Each key can be optional, which is what I think you're concerned about.
You'd want to pass something like
[{'mol1': 'HCN', 'rep1': 'SF', 'mol2': 'HCN', 'rep2': 'SF', 'same': True},
{'mol1': 'H20', 'rep1': 'SF', 'mol2': 'H20', 'rep2': 'SF', 'same': True}
...
]
where each dictionary could optionally include a probability. This could be done by a YAML snippet like
questions_to_ask:
type: list
values:
type: dict
values:
mol1:
type: str
rep1:
type: str
mol2:
type: str
rep2:
type: str
same:
type: bool
prob:
type: num
optional: true
(untested, but it's something like this).
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Those are the two types right now which can be solved by an optional argument. In future, we expect a few more types of inputs which will not be similar to these two.
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we expect a few more types of inputs which will not be similar to these two.
We can specify that we expect one of several input types, or we can introduce other keys to expect all input types. We can find a workaround for that when it comes up – this issue isn't blocking.
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Changed the input from string to a list of dictionaries.
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Related Issues (20)
- Use training and test distribution to generate instances for the RandomTrainTest algorithm.
- Generate an algorithm which can show a fixed training sequence to all participants.
- Same pretest and posttest across separate algorithms.
- Fix user data collection. HOT 1
- Assign Participants to correct condition HOT 1
- Use processAnswer to update number of questions a participant has seen once the front end is ready. HOT 1
- Use lock and synchronization to generate fixed questions for participants. HOT 1
- Handle different number of total questions for each participant.
- Investigate the dashboard.
- Write a parser to parse the next participant data into Molecule Equivalence Participant Data HOT 1
- Add guard questions.
- Handle different types of instructions and their parameters.
- Stop using index in FixedInstanceReader and make a base class for these dataset generators
- Add radio buttons to the widget for participants to submit answers
- Investigate how to delete entries from butler. HOT 1
- Use different locks for different algorithms in proessAnswer. HOT 1
- Collect more stats from users.
- Checkout butler increment. HOT 2
- Implement dashboard functions
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